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copies-rust: add smarter approach for merging small mapping with large mapping...
copies-rust: add smarter approach for merging small mapping with large mapping The current approach (finding the smaller updated set) works great when the mapping have similar size, but do a lot of unnecessary work when one side is tinier than the other one. So we do better in theses cases. See inline documentation for details. It give a sizeable boost to many of out slower cases: Repo Case Source-Rev Dest-Rev # of revisions old time new time Difference Factor time per rev --------------------------------------------------------------------------------------------------------------------------------------------------------------- mozilla-try x00000_revs_x_added_0_copies 6a320851d377 1ebb79acd503 : 363753 revs, 18.123103 s, 5.693818 s, -12.429285 s, × 0.3142, 15 µs/rev mozilla-try x00000_revs_x_added_x_copies 5173c4b6f97c 95d83ee7242d : 362229 revs, 17.907312 s, 5.677655 s, -12.229657 s, × 0.3171, 15 µs/rev mozilla-try x00000_revs_x000_added_x_copies 9126823d0e9c ca82787bb23c : 359344 revs, 17.684797 s, 5.563370 s, -12.121427 s, × 0.3146, 15 µs/rev mozilla-try x00000_revs_x0000_added_x0000_copies 8d3fafa80d4b eb884023b810 : 192665 revs, 2.881471 s, 2.864099 s, -0.017372 s, × 0.9940, 14 µs/rev mozilla-try x00000_revs_x00000_added_x000_copies 9b2a99adc05e 8e29777b48e6 : 382065 revs, 63.148971 s, 59.498652 s, -3.650319 s, × 0.9422, 155 µs/rev mozilla-try x00000_revs_x00000_added_x000_copies 9b2a99adc05e 8e29777b48e6 : 382065 revs, 63.148971 s, 59.498652 s, -3.650319 s, × 0.9422, 155 µs/rev ideally, the im-rs object would have a `merge` method, but it does not (yet) Full timing comparison below (they are one pathological case than become even worse, for unclear reason). Repo Case Source-Rev Dest-Rev # of revisions old time new time Difference Factor time per rev --------------------------------------------------------------------------------------------------------------------------------------------------------------- mercurial x_revs_x_added_0_copies ad6b123de1c7 39cfcef4f463 : 1 revs, 0.000043 s, 0.000042 s, -0.000001 s, × 0.9767, 42 µs/rev mercurial x_revs_x_added_x_copies 2b1c78674230 0c1d10351869 : 6 revs, 0.000105 s, 0.000104 s, -0.000001 s, × 0.9905, 17 µs/rev mercurial x000_revs_x000_added_x_copies 81f8ff2a9bf2 dd3267698d84 : 1032 revs, 0.004895 s, 0.004913 s, +0.000018 s, × 1.0037, 4 µs/rev pypy x_revs_x_added_0_copies aed021ee8ae8 099ed31b181b : 9 revs, 0.000194 s, 0.000191 s, -0.000003 s, × 0.9845, 21 µs/rev pypy x_revs_x000_added_0_copies 4aa4e1f8e19a 359343b9ac0e : 1 revs, 0.000050 s, 0.000050 s, +0.000000 s, × 1.0000, 50 µs/rev pypy x_revs_x_added_x_copies ac52eb7bbbb0 72e022663155 : 7 revs, 0.000115 s, 0.000112 s, -0.000003 s, × 0.9739, 16 µs/rev pypy x_revs_x00_added_x_copies c3b14617fbd7 ace7255d9a26 : 1 revs, 0.000289 s, 0.000288 s, -0.000001 s, × 0.9965, 288 µs/rev pypy x_revs_x000_added_x000_copies df6f7a526b60 a83dc6a2d56f : 6 revs, 0.010513 s, 0.010411 s, -0.000102 s, × 0.9903, 1735 µs/rev pypy x000_revs_xx00_added_0_copies 89a76aede314 2f22446ff07e : 4785 revs, 0.051474 s, 0.052852 s, +0.001378 s, × 1.0268, 11 µs/rev pypy x000_revs_x000_added_x_copies 8a3b5bfd266e 2c68e87c3efe : 6780 revs, 0.088086 s, 0.092828 s, +0.004742 s, × 1.0538, 13 µs/rev pypy x000_revs_x000_added_x000_copies 89a76aede314 7b3dda341c84 : 5441 revs, 0.062176 s, 0.063269 s, +0.001093 s, × 1.0176, 11 µs/rev pypy x0000_revs_x_added_0_copies d1defd0dc478 c9cb1334cc78 : 43645 revs, 0.720950 s, 0.711975 s, -0.008975 s, × 0.9876, 16 µs/rev pypy x0000_revs_xx000_added_0_copies bf2c629d0071 4ffed77c095c : 2 revs, 0.012897 s, 0.012771 s, -0.000126 s, × 0.9902, 6385 µs/rev pypy x0000_revs_xx000_added_x000_copies 08ea3258278e d9fa043f30c0 : 11316 revs, 0.121524 s, 0.124505 s, +0.002981 s, × 1.0245, 11 µs/rev netbeans x_revs_x_added_0_copies fb0955ffcbcd a01e9239f9e7 : 2 revs, 0.000082 s, 0.000082 s, +0.000000 s, × 1.0000, 41 µs/rev netbeans x_revs_x000_added_0_copies 6f360122949f 20eb231cc7d0 : 2 revs, 0.000109 s, 0.000111 s, +0.000002 s, × 1.0183, 55 µs/rev netbeans x_revs_x_added_x_copies 1ada3faf6fb6 5a39d12eecf4 : 3 revs, 0.000175 s, 0.000171 s, -0.000004 s, × 0.9771, 57 µs/rev netbeans x_revs_x00_added_x_copies 35be93ba1e2c 9eec5e90c05f : 9 revs, 0.000719 s, 0.000708 s, -0.000011 s, × 0.9847, 78 µs/rev netbeans x000_revs_xx00_added_0_copies eac3045b4fdd 51d4ae7f1290 : 1421 revs, 0.010426 s, 0.010608 s, +0.000182 s, × 1.0175, 7 µs/rev netbeans x000_revs_x000_added_x_copies e2063d266acd 6081d72689dc : 1533 revs, 0.015712 s, 0.015635 s, -0.000077 s, × 0.9951, 10 µs/rev netbeans x000_revs_x000_added_x000_copies ff453e9fee32 411350406ec2 : 5750 revs, 0.077353 s, 0.072072 s, -0.005281 s, × 0.9317, 12 µs/rev netbeans x0000_revs_xx000_added_x000_copies 588c2d1ced70 1aad62e59ddd : 66949 revs, 0.673930 s, 0.682732 s, +0.008802 s, × 1.0131, 10 µs/rev mozilla-central x_revs_x_added_0_copies 3697f962bb7b 7015fcdd43a2 : 2 revs, 0.000089 s, 0.000090 s, +0.000001 s, × 1.0112, 45 µs/rev mozilla-central x_revs_x000_added_0_copies dd390860c6c9 40d0c5bed75d : 8 revs, 0.000212 s, 0.000210 s, -0.000002 s, × 0.9906, 26 µs/rev mozilla-central x_revs_x_added_x_copies 8d198483ae3b 14207ffc2b2f : 9 revs, 0.000183 s, 0.000182 s, -0.000001 s, × 0.9945, 20 µs/rev mozilla-central x_revs_x00_added_x_copies 98cbc58cc6bc 446a150332c3 : 7 revs, 0.000595 s, 0.000594 s, -0.000001 s, × 0.9983, 84 µs/rev mozilla-central x_revs_x000_added_x000_copies 3c684b4b8f68 0a5e72d1b479 : 3 revs, 0.003117 s, 0.003102 s, -0.000015 s, × 0.9952, 1034 µs/rev mozilla-central x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 6 revs, 0.060197 s, 0.060234 s, +0.000037 s, × 1.0006, 10039 µs/rev mozilla-central x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 1593 revs, 0.006379 s, 0.006300 s, -0.000079 s, × 0.9876, 3 µs/rev mozilla-central x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 41 revs, 0.005008 s, 0.004817 s, -0.000191 s, × 0.9619, 117 µs/rev mozilla-central x000_revs_x000_added_x000_copies 7c97034feb78 4407bd0c6330 : 7839 revs, 0.065123 s, 0.065451 s, +0.000328 s, × 1.0050, 8 µs/rev mozilla-central x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 615 revs, 0.026404 s, 0.026282 s, -0.000122 s, × 0.9954, 42 µs/rev mozilla-central x0000_revs_xx000_added_x000_copies f78c615a656c 96a38b690156 : 30263 revs, 0.203456 s, 0.206873 s, +0.003417 s, × 1.0168, 6 µs/rev mozilla-central x00000_revs_x0000_added_x0000_copies 6832ae71433c 4c222a1d9a00 : 153721 revs, 1.929809 s, 1.935918 s, +0.006109 s, × 1.0032, 12 µs/rev mozilla-central x00000_revs_x00000_added_x000_copies 76caed42cf7c 1daa622bbe42 : 204976 revs, 2.825064 s, 2.827320 s, +0.002256 s, × 1.0008, 13 µs/rev mozilla-try x_revs_x_added_0_copies aaf6dde0deb8 9790f499805a : 2 revs, 0.000857 s, 0.000842 s, -0.000015 s, × 0.9825, 421 µs/rev mozilla-try x_revs_x000_added_0_copies d8d0222927b4 5bb8ce8c7450 : 2 revs, 0.000870 s, 0.000870 s, +0.000000 s, × 1.0000, 435 µs/rev mozilla-try x_revs_x_added_x_copies 092fcca11bdb 936255a0384a : 4 revs, 0.000161 s, 0.000165 s, +0.000004 s, × 1.0248, 41 µs/rev mozilla-try x_revs_x00_added_x_copies b53d2fadbdb5 017afae788ec : 2 revs, 0.001147 s, 0.001145 s, -0.000002 s, × 0.9983, 572 µs/rev mozilla-try x_revs_x000_added_x000_copies 20408ad61ce5 6f0ee96e21ad : 1 revs, 0.026640 s, 0.026500 s, -0.000140 s, × 0.9947, 26500 µs/rev mozilla-try x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 6 revs, 0.059849 s, 0.059407 s, -0.000442 s, × 0.9926, 9901 µs/rev mozilla-try x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 1593 revs, 0.006326 s, 0.006325 s, -0.000001 s, × 0.9998, 3 µs/rev mozilla-try x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 41 revs, 0.005188 s, 0.005171 s, -0.000017 s, × 0.9967, 126 µs/rev mozilla-try x000_revs_x000_added_x000_copies 1346fd0130e4 4c65cbdabc1f : 6657 revs, 0.067633 s, 0.066837 s, -0.000796 s, × 0.9882, 10 µs/rev mozilla-try x0000_revs_x_added_0_copies 63519bfd42ee a36a2a865d92 : 40314 revs, 0.306969 s, 0.314252 s, +0.007283 s, × 1.0237, 7 µs/rev mozilla-try x0000_revs_x_added_x_copies 9fe69ff0762d bcabf2a78927 : 38690 revs, 0.293370 s, 0.304160 s, +0.010790 s, × 1.0368, 7 µs/rev mozilla-try x0000_revs_xx000_added_x_copies 156f6e2674f2 4d0f2c178e66 : 8598 revs, 0.087159 s, 0.089223 s, +0.002064 s, × 1.0237, 10 µs/rev mozilla-try x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 615 revs, 0.027251 s, 0.026711 s, -0.000540 s, × 0.9802, 43 µs/rev mozilla-try x0000_revs_xx000_added_x000_copies 89294cd501d9 7ccb2fc7ccb5 : 97052 revs, 3.010011 s, 3.243010 s, +0.232999 s, × 1.0774, 33 µs/rev mozilla-try x0000_revs_x0000_added_x0000_copies e928c65095ed e951f4ad123a : 52031 revs, 0.753434 s, 0.756500 s, +0.003066 s, × 1.0041, 14 µs/rev mozilla-try x00000_revs_x_added_0_copies 6a320851d377 1ebb79acd503 : 363753 revs, 18.123103 s, 5.693818 s, -12.429285 s, × 0.3142, 15 µs/rev mozilla-try x00000_revs_x00000_added_0_copies dc8a3ca7010e d16fde900c9c : 34414 revs, 0.583206 s, 0.590904 s, +0.007698 s, × 1.0132, 17 µs/rev mozilla-try x00000_revs_x_added_x_copies 5173c4b6f97c 95d83ee7242d : 362229 revs, 17.907312 s, 5.677655 s, -12.229657 s, × 0.3171, 15 µs/rev mozilla-try x00000_revs_x000_added_x_copies 9126823d0e9c ca82787bb23c : 359344 revs, 17.684797 s, 5.563370 s, -12.121427 s, × 0.3146, 15 µs/rev mozilla-try x00000_revs_x0000_added_x0000_copies 8d3fafa80d4b eb884023b810 : 192665 revs, 2.881471 s, 2.864099 s, -0.017372 s, × 0.9940, 14 µs/rev mozilla-try x00000_revs_x00000_added_x0000_copies 1b661134e2ca 1ae03d022d6d : 228985 revs, 101.062002 s, 113.297287 s, +12.235285 s, × 1.1211, 494 µs/rev mozilla-try x00000_revs_x00000_added_x000_copies 9b2a99adc05e 8e29777b48e6 : 382065 revs, 63.148971 s, 59.498652 s, -3.650319 s, × 0.9422, 155 µs/rev Differential Revision: https://phab.mercurial-scm.org/D9491

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__init__.py
126 lines | 3.9 KiB | text/x-python | PythonLexer
# __init__.py - asv benchmark suite
#
# Copyright 2016 Logilab SA <contact@logilab.fr>
#
# This software may be used and distributed according to the terms of the
# GNU General Public License version 2 or any later version.
# "historical portability" policy of contrib/benchmarks:
#
# We have to make this code work correctly with current mercurial stable branch
# and if possible with reasonable cost with early Mercurial versions.
'''ASV (https://asv.readthedocs.io) benchmark suite
Benchmark are parameterized against reference repositories found in the
directory pointed by the REPOS_DIR environment variable.
Invocation example:
$ export REPOS_DIR=~/hgperf/repos
# run suite on given revision
$ asv --config contrib/asv.conf.json run REV
# run suite on new changesets found in stable and default branch
$ asv --config contrib/asv.conf.json run NEW
# display a comparative result table of benchmark results between two given
# revisions
$ asv --config contrib/asv.conf.json compare REV1 REV2
# compute regression detection and generate ASV static website
$ asv --config contrib/asv.conf.json publish
# serve the static website
$ asv --config contrib/asv.conf.json preview
'''
from __future__ import absolute_import
import functools
import os
import re
from mercurial import (
extensions,
hg,
ui as uimod,
util,
)
basedir = os.path.abspath(
os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir)
)
reposdir = os.environ['REPOS_DIR']
reposnames = [
name
for name in os.listdir(reposdir)
if os.path.isdir(os.path.join(reposdir, name, ".hg"))
]
if not reposnames:
raise ValueError("No repositories found in $REPO_DIR")
outputre = re.compile(
(
r'! wall (\d+.\d+) comb \d+.\d+ user \d+.\d+ sys '
r'\d+.\d+ \(best of \d+\)'
)
)
def runperfcommand(reponame, command, *args, **kwargs):
os.environ["HGRCPATH"] = os.environ.get("ASVHGRCPATH", "")
# for "historical portability"
# ui.load() has been available since d83ca85
if util.safehasattr(uimod.ui, "load"):
ui = uimod.ui.load()
else:
ui = uimod.ui()
repo = hg.repository(ui, os.path.join(reposdir, reponame))
perfext = extensions.load(
ui, 'perfext', os.path.join(basedir, 'contrib', 'perf.py')
)
cmd = getattr(perfext, command)
ui.pushbuffer()
cmd(ui, repo, *args, **kwargs)
output = ui.popbuffer()
match = outputre.search(output)
if not match:
raise ValueError("Invalid output {}".format(output))
return float(match.group(1))
def perfbench(repos=reposnames, name=None, params=None):
"""decorator to declare ASV benchmark based on contrib/perf.py extension
An ASV benchmark is a python function with the given attributes:
__name__: should start with track_, time_ or mem_ to be collected by ASV
params and param_name: parameter matrix to display multiple graphs on the
same page.
pretty_name: If defined it's displayed in web-ui instead of __name__
(useful for revsets)
the module name is prepended to the benchmark name and displayed as
"category" in webui.
Benchmarks are automatically parameterized with repositories found in the
REPOS_DIR environment variable.
`params` is the param matrix in the form of a list of tuple
(param_name, [value0, value1])
For example [(x, [a, b]), (y, [c, d])] declare benchmarks for
(a, c), (a, d), (b, c) and (b, d).
"""
params = list(params or [])
params.insert(0, ("repo", repos))
def decorator(func):
@functools.wraps(func)
def wrapped(repo, *args):
def perf(command, *a, **kw):
return runperfcommand(repo, command, *a, **kw)
return func(perf, *args)
wrapped.params = [p[1] for p in params]
wrapped.param_names = [p[0] for p in params]
wrapped.pretty_name = name
return wrapped
return decorator